The vehicle/robot localization technology has been researched for many years, and many of the proposed benefits have been demonstrated in varied applications. Many categories of maps have been developed and used in vehicle/robot localization, such as a point map which consists of laser points, a grid map which separates the environment into a grid with each grid cell recording whether it is occupied by something as well as the probability of the occupancy, a geometric primitive map which uses one or more types of geometric primitives to represent entities in the environment, a feature map which mainly consists of feature points and their corresponding descriptors extracted from other types of data (e.g., a point cloud, a camera image, etc.), a Normal distribution transform (NDT) map which uses uni-weighted Gaussian Mixture Model to represent the environment, with each Gaussian distribution modeling a unique grid cell of the environment, a Normal distribution transform Occupancy (NDT-OM) map which separates the environment into grid, within each grid cell of which a Gaussian distribution is calculated among the data points in the cell and a weight which represents the occupancy probability of this cell is maintained for the Gaussian distribution.
The existing method and system which are used for a vehicle/robot to locate itself by using sensors like odometry, GPS, laser scanner, camera etc. mainly involve localization based on matching laser points acquired by the vehicle/robot with a grid map, localization based on matching the laser points with a point cloud map, and localization based on matching laser point features with point cloud map features.